Hangzhou Dianzi University

About: Hangzhou Dianzi University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 14547 authors who have published 14941 publications receiving 144766 citations. The organization is also known as: Hangzhou Institute of Electrical Engineering & Hángzhōu diànzǐ kējì dàxué.

An empirical examination of continuance intention of mobile payment services

Abstract: Retaining users and facilitating their continuance usage are crucial for mobile payment service providers. Drawing on the information systems success model and flow theory, this research identified the factors affecting continuance intention of mobile payment. We conducted data analysis with structural equation modeling. The results indicated that service quality is the main factor affecting trust, whereas system quality is the main factor affecting satisfaction. Information quality and service quality affect flow. Trust, flow and satisfaction determine continuance intention of mobile payment. The results imply that service providers need to offer quality system, information and services in order to facilitate users' continuance usage of mobile payment. Highlights? Service quality is the main factor affecting trust. ? System quality is the main factor affecting satisfaction. ? Information quality and service quality affect flow. ? Trust, flow and satisfaction determine continuance usage of mobile payment.

Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering

Abstract: Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multimodal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multimodal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a ‘co-attention’ mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-theart performance on the real-world VQA dataset. Code available at https://github.com/yuzcccc/mfb.